Preconditioning of the background error covariance matrix in data assimilation for the Caspian Sea

Data Assimilation (DA) is an uncertainty quantification technique used for improving numerical forecasted results by incorporating observed data into prediction models. As a crucial point into DA models is the ill conditioning of the covariance matrices involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here we present first studies concerning the introduction of two different preconditioning methods in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea by using the Regional Ocean Modeling System (ROMS) with observations provided by the Group of High resolution sea surface temperature (GHRSST). We also present the algorithmic strategies we employ

A scalable approach for Variational Data Assimilation

Data assimilation (DA) is a methodology for combining mathematical models simulating complex systems (the background knowledge) and measurements (the reality or observational data) in order to improve the estimate of the system state (the forecast). The DA is an inverse and ill posed problem usually used to handle a huge amount of data, so, it is a large and computationally expensive problem. Here we focus on scalable methods that makes DA applications feasible for a huge number of background data and observations. We present a scalable algorithm for solving variational DA which is highly parallel. We provide a mathematical formalization of this approach and we also study the performance of the resulted algorith

Toward the S3DVAR data assimilation software for the Caspian Sea

Data Assimilation (DA) is an uncertainty quantification technique used to incorporate observed data into a prediction model in order to improve numerical forecasted results. The forecasting model used for producing oceanographic prediction into the Caspian Sea is the Regional Ocean Modeling System (ROMS). Here we propose the computational issues we are facing in a DA software we are developing (we named S3DVAR) which implements a Scalable Three Dimensional Variational Data Assimilation model for assimilating sea surface temperature (SST) values collected into the Caspian Sea with observations provided by the Group of High resolution sea surface temperature (GHRSST). We present the algorithmic strategies we employ and the numerical issues on data collected in two of the months which present the most significant variability in water temperature: August and March

Enhancing CFD-LES air pollution prediction accuracy using data assimilation

It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude

Influence of tumor microenvironment and fibroblast population plasticity on melanoma growth, therapy resistance and immunoescape

Cutaneous melanoma (CM) tissue represents a network constituted by cancer cells and tumor microenvironment (TME). A key feature of CM is the high structural and cellular plasticity of TME, allowing its evolution with disease and adaptation to cancer cell and environmental alter-ations. In particular, during melanoma development and progression each component of TME by interacting with each other and with cancer cells is subjected to dramatic structural and cellular modifications. These alterations affect extracellular matrix (ECM) remodelling, phenotypic profile of stromal cells, cancer growth and therapeutic response. The stromal fibroblast populations of the TME include normal fibroblasts and melanoma‐associated fibroblasts (MAFs) that are highly abun-dant and flexible cell types interacting with melanoma and stromal cells and differently influencing CM outcomes. The shift from the normal microenvironment to TME and from normal fibroblasts to MAFs deeply sustains CM growth. Hence, in this article we review the features of the normal mi-croenvironment and TME and describe the phenotypic plasticity of normal dermal fibroblasts and MAFs, highlighting their roles in normal skin homeostasis and TME regulation. Moreover, we dis-cuss the influence of MAFs and their secretory profiles on TME remodelling, melanoma progres-sion, targeted therapy resistance and immunosurveillance, highlighting the cellular interactions, the signalling pathways and molecules involved in these processes

Machine learning for earth system observation and prediction

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A Bayesian Updating Scheme for Pandemics: Estimating the Infection Dynamics of COVID-19

Epidemic models play a key role in understanding and responding to the emerging COVID-19 pandemic. Widely used compartmental models are static and are of limited use to evaluate intervention strategies of combatting the pandemic. Applying the technology of data assimilation, we propose a Bayesian updating approach for estimating epidemiological parameters using observable information to assess the impacts of different intervention strategies. We adopt a concise renewal model and propose new parameters by disentangling the reduction of instantaneous reproduction number Rt into mitigation and suppression factors to quantify intervention impacts at a finer granularity. A data assimilation framework is developed to estimate these parameters including constructing an observation function and developing a Bayesian updating scheme. A statistical analysis framework is built to quantify the impacts of intervention strategies by monitoring the evolution of the estimated parameters. We reveal the intervention impacts in European countries and Wuhan and the resurgence risk in the United States

Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows

The separation of liquid–liquid dispersions in horizontal pipes is common in many industrial sectors. It remains challenging, however, to predict the separation characteristics of the flow evolution due to the complex flow mechanisms. In this work, Computational Fluid Dynamics (CFD) simulations of the silicone oil and water two-phase flow in a horizontal pipe are performed. Several cases are explored with different mixture velocities and oil fractions (15%-60%). OpenFOAM (version 8.0) is used to perform Eulerian-Eulerian simulations coupled with population balance models. The ‘blending factor’ in the multiphaseEulerFoam solver captures the retardation of the droplet rising and coalescing due to the complex flow behaviour in the dense packed layer (DPL). The blending treatment provides a feasible compensation mechanism for the mesoscale uncertainties of droplet flow and coalescence through the DPL and its adjacent layers. In addition, the influence of the turbulent dispersion force is also investigated, which can improve the prediction of the radial distribution of concentrations but worsen the separation characteristics along the flow direction. Although the simulated concentration distribution and layer heights agree with the experiments only qualitatively, this work demonstrates how improvements in drag and coalescence modelling can be made to enhance the prediction accuracy

Insufficient Control of Blood Pressure and Incident Diabetes

Incidence of type 2 diabetes might be associated with preexisting hypertension. There is no information on whether incident diabetes is predicted by blood pressure control. We evaluated the hazard of diabetes in relation to blood pressure control in treated hypertensive patients.Nondiabetic, otherwise healthy, hypertensive patients (N = 1,754, mean +/- SD age 52 +/- 11 years, 43\% women) participated in a network over 3.4 +/- 1 years of follow-up. Blood pressure was considered uncontrolled if systolic was >or=140 mmHg and/or diastolic was >or=90 mmHg at the last outpatient visit. Diabetes was defined according to American Diabetes Association guidelines.Uncontrolled blood pressure despite antihypertensive treatment was found in 712 patients (41\%). At baseline, patients with uncontrolled blood pressure were slightly younger than patients with controlled blood pressure (51 +/- 11 vs. 53 +/- 12 years, P < 0.001), with no differences in sex distribution, BMI, duration of hypertension, baseline blood pressure, fasting glucose, serum creatinine and potassium, lipid profile, or prevalence of metabolic syndrome. During follow-up, 109 subjects developed diabetes. Incidence of diabetes was significantly higher in patients with uncontrolled (8\%) than in those with controlled blood pressure (4\%, odds ratio 2.08, P < 0.0001). In Cox regression analysis controlling for baseline systolic blood pressure and BMI, family history of diabetes, and physical activity, uncontrolled blood pressure doubled the risk of incident diabetes (hazard ratio [HR] 2.10, P < 0.001), independently of significant effects of age (HR 1.02 per year, P = 0.03) and baseline fasting glucose (HR 1.10 per mg/dl, P < 0.001).In a large sample of treated nondiabetic hypertensive subjects, uncontrolled blood pressure is associated with twofold increased risk of incident diabetes independently of age, BMI, baseline blood pressure, or fasting glucose